A Modularly Designed Controllable Generative Frameworkfor Glioma and MRI Editing Via Style Representations Enhancement DOI
Ling Qi, Zhengang Jiang, Weili Shi

et al.

Published: Jan. 1, 2024

Language: Английский

Using diffusion models to generate synthetic labeled data for medical image segmentation DOI
Daniel Saragih, Atsuhiro Hibi, Pascal N. Tyrrell

et al.

International Journal of Computer Assisted Radiology and Surgery, Journal Year: 2024, Volume and Issue: 19(8), P. 1615 - 1625

Published: June 20, 2024

Language: Английский

Citations

7
Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment DOI Creative Commons
Saúl Cano-Ortiz,

Eugenio Sainz-Ortiz,

L. Lloret Iglesias

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102745 - 102745

Published: Aug. 18, 2024

Language: Английский

Citations

4
Image synthesis with class‐aware semantic diffusion models for surgical scene segmentation DOI Creative Commons

Yihang Zhou,

Rebecca Towning,

Zaid Awad

et al.

Healthcare Technology Letters, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract Surgical scene segmentation is essential for enhancing surgical precision, yet it frequently compromised by the scarcity and imbalance of available data. To address these challenges, semantic image synthesis methods based on generative adversarial networks diffusion models have been developed. However, often yield non‐diverse images fail to capture small, critical tissue classes, limiting their effectiveness. In response, a class‐aware model (CASDM), novel approach which utilizes maps as conditions tackle data proposed. Novel mean squared error self‐perceptual loss functions defined prioritize critical, less visible thereby quality relevance. Furthermore, authors' knowledge, they are first generate multi‐class using text prompts in fashion specify contents. These then used CASDM images, datasets training validating models. This evaluation assesses both downstream performance, demonstrates strong effectiveness generalisability producing realistic image‐map pairs, significantly advancing across diverse challenging datasets.

Language: Английский

Citations

0
Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation DOI Creative Commons
Ojonugwa Oluwafemi Ejiga Peter, Opeyemi Adeniran, Adetokunbo MacGregor John-Otumu

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 155 - 155

Published: March 9, 2025

The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement artificial intelligence (AI) for medical applications, especially colorectal cancer detection. Models trained with limited diversity often display biases, when utilized on disadvantaged groups. Generative models (e.g., DALL-E 2, Vector-Quantized Adversarial Network (VQ-GAN)) have been used to generate images but not colonoscopy data intelligent augmentation. This study developed an effective method producing synthetic image data, which can be train advanced diagnostic robust detection treatment. Text-to-image synthesis was performed using fine-tuned Visual Large Language (LLMs). Stable Diffusion DreamBooth Low-Rank Adaptation produce that look authentic, average Inception score 2.36 across three datasets. validation accuracy various classification Big Transfer (BiT), Fixed Resolution Residual Next Generation (FixResNeXt), Efficient Neural (EfficientNet) were 92%, 91%, 86%, respectively. Vision Transformer (ViT) Data-Efficient Image Transformers (DeiT) had rate 93%. Secondly, segmentation polyps, ground truth masks are generated Segment Anything Model (SAM). Then, five (U-Net, Pyramid Scene Parsing (PSNet), Feature (FPN), Link (LinkNet), Multi-scale Attention (MANet)) adopted. FPN produced excellent results, Intersection Over Union (IoU) 0.64, F1 0.78, a recall 0.75, Dice coefficient 0.77. demonstrates strong performance in terms both overlap metrics, particularly results balanced capability as shown by high coefficient. highlights how AI-generated improve analysis, is critical early

Language: Английский

Citations

0
Masked Conditional Diffusion Model for Enhancing Deepfake Detection DOI

Tiewen Chen,

Shanmin Yang,

Shu Hu

et al.

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 7

Published: June 30, 2024

Language: Английский

Citations

2
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning DOI

Woojung Han,

Chanyoung Kim,

Dayun Ju

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 56 - 66

Published: Jan. 1, 2024

Language: Английский

Citations

1
Data Augmentation in Class-Conditional Diffusion Model for Semi-Supervised Medical Image Segmentation DOI
Jiaying Zhang, Guibo Luo,

Zi’Ang Zhang

et al.

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: 27, P. 1 - 8

Published: June 30, 2024

Language: Английский

Citations

0
Uncertainty-Aware Diffusion-Based Adversarial Attack for Realistic Colonoscopy Image Synthesis DOI

Minjae Jeong,

Hyuna Cho,

Sung‐Yoon Jung

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 647 - 658

Published: Jan. 1, 2024

Language: Английский

Citations

0
Multi-frequency and Smoke Attention-Aware Learning Based Diffusion Model for Removing Surgical Smoke DOI
Hao Li,

Xiangyu Zhai,

Jie Xue

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 47 - 56

Published: Jan. 1, 2024

Language: Английский

Citations

0
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development DOI
Yuncheng Jiang, Yiwen Hu, Zixun Zhang

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 732 - 742

Published: Jan. 1, 2024

Language: Английский

Citations

0